Essays about: "multi-armed"
Showing result 1 - 5 of 19 essays containing the word multi-armed.
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1. Risk-Averse Multi-Armed Bandit Problem with Multiple Plays
University essay from Göteborgs universitet/Institutionen för data- och informationsteknikAbstract : This study aims to construct an efficient heuristic, referred to as RA, for a riskaverse Markovian multi-armed bandit problem (MAB) with multiple plays. The RA incorporates risk-aversion and multiple plays by modifying the Gittins index strategy. READ MORE
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2. An Empirical Survey of Bandits in an Industrial Recommender System Setting
University essay from Göteborgs universitet/Institutionen för data- och informationsteknikAbstract : In this thesis, the effects of incorporating unstructured data—images in the wild—in contextual multi-armed bandits are investigated, when used within a recommender system setting, which focuses on picture-based content suggestion. The idea is to employ image features, extracted by a pre-trained convolutional neural network, and study the resulting bandit behaviors when including respective excluding this information in the typical context creation, which normally relies on structured data sources—such as metadata. READ MORE
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3. Causal Reinforcement Learning for Bandits with Unobserved Confounders
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Reinforcement Learning (RL) has been recognized as a valuable tool in various fields. However, its application is limited by its reliance on extensive data through a trial-and-error approach and challenges in generalizing learned policies. READ MORE
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4. Exploration-Exploitation Trade-off Approaches in Multi-Armed Bandit
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Multi-armed bandit, a popular framework for sequential decision-making problems, has recently gained significant attention due to numerous applications. In Multi-armed Bandit, an agent faces the central challenge of choosing exploitation of its belief to hopefully gain a high reward and exploration to improve its knowledge of the environment, and any good strategy has to efficiently balance between the two actions. READ MORE
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5. Edge Compute Offloading Strategies using Heuristic and Reinforcement Learning Techniques.
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The emergence of 5G alongside the distributed computing paradigm called Edge computing has prompted a tremendous change in the industry through the opportunity for reducing network latency and energy consumption and providing scalability. Edge computing extends the capabilities of users’ resource-constrained devices by placing data centers at the edge of the network. READ MORE
